22 citations · 42 across the 20 of their papers we have counts for
5 papers · 1 filter
Beyond Defensive Reporting: Machine Learning for Active Anti-Money Laundering Control in Insurance
Dara Goldar, Geir Kjetil Ferkingstad Sandve, Martin Jullum
Money laundering through insurance claims poses a threat to insurers both through fraudulent payouts and reputational and regulatory risk. Despite this, little research has examine…
What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models
Jan Kapar, Niklas Koenen, Martin Jullum
Evaluating synthetic tabular data is challenging, since they can differ from the real data in so many ways. There exist numerous metrics of synthetic data quality, ranging from sta…
shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python
Martin Jullum, Lars Henry Berge Olsen, Jon Lachmann +1
This paper introduces the shapr R package, a versatile tool for generating Shapley value-based prediction explanations for machine learning and statistical regression models. Moreo…
Improving the Weighting Strategy in KernelSHAP
Lars Henry Berge Olsen, Martin Jullum
In Explainable AI (XAI), Shapley values are a popular model-agnostic framework for explaining predictions made by complex machine learning models. The computation of Shapley values…
Finding Money Launderers Using Heterogeneous Graph Neural Networks
Fredrik Johannessen, Martin Jullum
Current anti-money laundering (AML) systems, predominantly rule-based, exhibit notable shortcomings in efficiently and precisely detecting instances of money laundering. As a resul…